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Image classification is an important application for deep learning. With the advent of quantum technology, quantum neural networks (QNNs) have become the focus of research. Traditional deep learning-based image classification involves using a convolutional neural network (CNN) to extract features from the image and a multi-layer perceptron (MLP) network to create the decision boundaries. However, quantum circuits with parameters can extract rich features from images and also create complex decision boundaries. This paper proposes a hybrid QNN (H-QNN) model designed for binary image classification that capitalizes on the strengths of quantum computing and classical neural networks. Our H-QNN model uses a compact, two-qubit quantum circuit integrated with a classical convolutional architecture, making it highly efficient for computation on noisy intermediate-scale quantum (NISQ) devices that are currently leading the way in practical quantum computing applications. Our H-QNN model significantly enhances classification accuracy, achieving a 90.1% accuracy rate on binary image datasets. In addition, we have extensively evaluated baseline CNN and our proposed H-QNN models for image retrieval tasks. The obtained quantitative results exhibit the generalization of our H-QNN for downstream image retrieval tasks. Furthermore, our model addresses the issue of overfitting for small datasets, making it a valuable tool for practical applications.

This study presents a novel approach to cryptographic algorithm design that harnesses the power of recurrent neural networks. Unlike traditional mathematical-based methods, neural networks offer nonlinear models that excel at capturing chaotic behavior within systems. We employ a recurrent neural network trained on Monte Carlo estimation to predict future states and generate confusion components. The resulting highly nonlinear substitution boxes exhibit exceptional characteristics, with a maximum nonlinearity of 114 and low linear and differential probabilities. To evaluate the efficacy of our methodology, we employ a comprehensive range of traditional and advanced metrics for assessing randomness and cryptanalytics. Comparative analysis against state-of-the-art methods demonstrates that our developed nonlinear confusion component offers remarkable efficiency for block-cipher applications.

In the current era of digital communication, image security is a top priority as sending images, which often contain personal or confidential information, over the Internet poses security risks. Many encryption algorithms have been proposed in the literature to address this issue. However, these algorithms have limitations, such as low-key space, high computational overhead, and susceptibility to differential attacks. To address these limitations, this paper proposes Z-Crypt, a novel image encryption approach that combines a substitution-permutation network (SPN) and a chaotic logistic map (CLM) with the Chirp Z-Transform (CZT) to enhance security and resist attacks. The CLM-generated matrices introduce confusion and diffusion, while SPN creates a dissociation of the cipher from the plaintext. Finally, the CZT strengthens the encryption by transforming the image into the frequency domain, creating multiple layers of confusion and diffusion to produce a robust encryption algorithm. We have evaluated the proposed Z-Crypt’s security using various metrics, including correlation coefficient, entropy, peak signal-to-noise ratio (PSNR), and key sensitivity analysis. Experimental results verify that the proposed algorithm outperforms existing methods, achieving high security while maintaining computational efficiency.

The Tor network, renowned for its provision of online privacy and anonymity, faces the constant threat of correlation attacks that aim to compromise user identities. For almost two decades, these correlation attacks were based on statistical methods. However, in recent years, deep learning-based correlation attacks have been introduced to make them more accurate. Nevertheless, in addition to being accurate, fast correlation attacks on Tor are crucial for assessing the real-world viability of such attacks because reduced correlation time aids in estimating its practical implications. Moreover, a reduction in correlation time also helps improve efficiency and ensures practical relevance of the attack. The existing state-of-the-art implementation of a correlation attack on Tor suffers from slow performance and large memory requirements. For instance, training the model required 133 GB of memory, and correlating 10,000 flows takes about 976 seconds. In this paper, we present a novel GPU-based correlation strategy and a fast traffic flow loading technique to reduce time complexity by $7.12\times $ compared to existing methods. Our computational approach, reliant on PyCUDA, facilitates the parallelization of operations used in the attack, thereby enabling efficient execution through the utilization of GPU architecture. Leveraging these two approaches, we introduced an improved correlation attack, which shows high accuracy and fast performance compared to state-of-the-art methods. Moreover, we address resource limitation issues by reducing memory consumption by 47.37% during the training phase, which allows the model to be trained with much fewer resources.

This paper presents the employment of a DPA attack on the NIST (National Institute of Standards and Technology) standardized AES (advance encryption standard) protocol for key retrieval and prevention. Towards key retrieval, we applied the DPA attack on AES to obtain a 128-bit secret key by measuring the power traces of the computations involved in the algorithm. In resistance to the DPA attack, we proposed a countermeasure, or a new modified masking scheme, comprising (i) Boolean and (ii) multiplicative masking, for linear and non-linear operations of AES, respectively. Furthermore, we improved the complexity involved in Boolean masking by introducing Rebecca’s approximation. Moreover, we provide a novel solution to tackle the zero mask problem in multiplicative masking. To evaluate the power traces, we propose our custom correlation technique, which results in a decrease in the calculation time. The synthesis results for original implementation (without countermeasure) and inclusion of countermeasure are given on a Zynq 7020 FPGA (Artix-7 device). It takes 424 FPGA slices when implemented without considering the countermeasure, whereas 714 slices are required to implement AES with the inclusion of the proposed countermeasure. Consequently, the implementation results provide the acceptability of this work for area-constrained applications that require prevention against DPA attacks.

An unmanned free-swimming submersible (UFSS) is designed to perform certain tasks in water without interposing humans. The vehicle’s control is achieved by integrating mathematical (analog) and non-mathematical (embedded) controllers. The main goal of integrated controllers is to overcome the environmental disturbances and noise of the sensor data. These disturbances, as well as the noise data, are generated during steering, diving, and speed control. The amplitude of disturbances and noise varies with the depth and intensity of water waves. This article presents a robust hybrid linear quadratic regulator (HLQR) controller for UFSS. The presented controller targets the desired state of the UFSS in the presence of a disturbing environment. The hybrid approach is achieved by employing: (1) two linear quadratic regulators or controllers and (2) a mathematical structure of the Riccati equation. Consequently, the proposed HLQR controller is integrated into the UFSS system to evaluate the response in terms of settling time, rise time, overshoot, and steady-state error. Furthermore, the robustness of the HLQR is investigated by considering the feedback to step response and hydrodynamic disturbances. The implementation results reveal that the proposed controller outperforms state of the art controllers, such as proportional-integral-derivative and lead-compensator controllers.

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.